What question did this study set out to answer?

The aim is to develop an accurate real-time system for detecting driver drowsiness using facial behavior.

June 13, 2026Open Access

Dynamic driver drowsiness detection with attention enhanced convolutional neural networks for real time monitoring and road safety applications

Key Points

The aim is to develop an accurate real-time system for detecting driver drowsiness using facial behavior.
Utilized deep learning-based facial analysis with pre-trained models and custom CNN.
Integrated attention mechanism to enhance classification based on eye and mouth behavior.
Evaluated performance on the NTHU-DDD dataset, measuring accuracy and relevant metrics.
Achieved an accuracy of 99.63% with perfect precision, recall, and F1-score.
The attention mechanism improved detection by focusing on significant facial regions.
Demonstrated potential for deployment in driver monitoring systems.

Abstract

Driver fatigue remains a major contributor to traffic accidents worldwide, underscoring the need for accurate and timely drowsiness detection systems. This work presents a real-time driver drowsiness detection framework utilizing deep learning-based facial analysis. The approach integrates pre-trained models (VGG19, VGG16, ResNet150, and DenseNet201) and a custom convolutional neural network (CNN) with an attention mechanism to classify drowsiness based on eye and mouth behavior. These models were fine-tuned and evaluated on the NTHU-DDD dataset. Experimental results show that the proposed CNN with attention mechanism achieves the highest performance, with an accuracy of 99.63% and perfect precision, recall, and F1-score. The attention mechanism further improves detection by emphasizing relevant facial regions. This framework demonstrates the potential for deployment in real-world driver monitoring systems and contributes to advancing driver-assistance technologies.

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